Adjunct Instructor in Applied Data Science and Decision Analytics
Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 100 Foundations of Applied Data Science and Decision Analytics for the Fall 1 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics. This course will introduce the data lifecycle, reproducible workflows, and ethical communication of analytical insights. Students learn to structure analytic questions, organize projects in collaborative repositories, and communicate uncertainty effectively. Core Course Responsibilities Summary Course Logistics and Facilitation: Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms. Instructor Presence and Engagement: Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation. Individual Feedback and Grading: Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe. Professional Conduct and Standards: Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term. Qualifications: Required: Advanced degree (Master's or Ph.D) in Computer Science, Analytics, Strategic Analytics, Strategic Leadership, Information Design, Data Science, Software Engineering, or related field. Experience using BI and visual analytics tools in industry or consulting contexts. Experience in data science and decision analytics, including data lifecycle Demonstrate ability to frame data problems and decision-focused questions, and to implement collaborative, version-controlled analysis. Experience in business intelligence, data visualization, and analytics storytelling, with the ability to translate insights into strategic actions. At least 1 year of teaching or training experience (preferably online/asynchronous) Experience with online instruction Excellent communication and teaching skills in an online learning environment. Preferred: Prior online teaching experience at the graduate level Knowledge of global learner personas and culturally responsive pedagogy Familiarity with Moodle LMS and digital authoring tools (e.g., H5P) Familiarity with SQL, Python, and/or R. Interested candidates should submit: A cover letter highlighting relevant qualifications and teaching experience. A current CV or resume. Contact information for three professional references. Application review begins May 27, 2026 though we will continue to accept submissions on an ongoing basis. This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509. Compensation for this positon is: $6573.15 Pay Range Disclosure The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements. Equal Opportunity Statement Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class"). Apply To This Job